ultralytics 8.0.153 YOLO Tasks Cleanup (ultralytics#4314)

docs/datasets/index.md

@@ -52,6 +52,12 @@ Image classification is a computer vision task that involves categorizing an ima
 - [Imagewoof](classify/imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
 - [MNIST](classify/mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
 
+## [Oriented Bounding Boxes (OBB)](obb/index.md)
+
+Oriented Bounding Boxes (OBB) is a method in computer vision for detecting angled objects in images using rotated bounding boxes, often applied to aerial and satellite imagery.
+
+- [DOTAv2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
+
 ## [Multi-Object Tracking](track/index.md)
 
 Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence.

docs/datasets/obb/index.md

@@ -1,12 +1,12 @@
 ---
 comments: true
-description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with the Ultralytics YOLO model. Grasp the nuances of using and converting datasets to this format.
+description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with Ultralytics YOLO models. Grasp the nuances of using and converting datasets to this format.
 keywords: Ultralytics, YOLO, oriented bounding boxes, OBB, dataset formats, label formats, DOTA v2, data conversion
 ---
 
-# Oriented Bounding Box Datasets Overview
+# Oriented Bounding Box (OBB) Datasets Overview
 
-Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide elucidates the various OBB dataset formats compatible with the Ultralytics YOLO model, offering insights into their structure, application, and methods for format conversions.
+Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
 
 ## Supported OBB Dataset Formats
 

docs/modes/train.md

@@ -160,7 +160,7 @@ Training settings for YOLO models refer to the various hyperparameters and confi
 | `single_cls`      | `False`  | train multi-class data as single-class                                            |
 | `rect`            | `False`  | rectangular training with each batch collated for minimum padding                 |
 | `cos_lr`          | `False`  | use cosine learning rate scheduler                                                |
-| `close_mosaic`    | `0`      | (int) disable mosaic augmentation for final epochs                                |
+| `close_mosaic`    | `10`     | (int) disable mosaic augmentation for final epochs (0 to disable)                 |
 | `resume`          | `False`  | resume training from last checkpoint                                              |
 | `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]                   |
 | `fraction`        | `1.0`    | dataset fraction to train on (default is 1.0, all images in train set)            |

docs/usage/cfg.md

@@ -102,7 +102,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
 | `single_cls`      | `False`  | train multi-class data as single-class                                            |
 | `rect`            | `False`  | rectangular training with each batch collated for minimum padding                 |
 | `cos_lr`          | `False`  | use cosine learning rate scheduler                                                |
-| `close_mosaic`    | `0`      | (int) disable mosaic augmentation for final epochs                                |
+| `close_mosaic`    | `10`     | (int) disable mosaic augmentation for final epochs (0 to disable)                 |
 | `resume`          | `False`  | resume training from last checkpoint                                              |
 | `amp`             | `True`   | Automatic Mixed Precision (AMP) training, choices=[True, False]                   |
 | `fraction`        | `1.0`    | dataset fraction to train on (default is 1.0, all images in train set)            |

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.152'
+__version__ = '8.0.153'
 
 from ultralytics.hub import start
 from ultralytics.models import RTDETR, SAM, YOLO

ultralytics/cfg/default.yaml

@@ -27,7 +27,7 @@ deterministic: True  # (bool) whether to enable deterministic mode
 single_cls: False  # (bool) train multi-class data as single-class
 rect: False  # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
 cos_lr: False  # (bool) use cosine learning rate scheduler
-close_mosaic: 10  # (int) disable mosaic augmentation for final epochs
+close_mosaic: 10  # (int) disable mosaic augmentation for final epochs (0 to disable)
 resume: False  # (bool) resume training from last checkpoint
 amp: True  # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
 fraction: 1.0  # (float) dataset fraction to train on (default is 1.0, all images in train set)

ultralytics/engine/model.py

@@ -9,7 +9,7 @@
 from ultralytics.engine.exporter import Exporter
 from ultralytics.hub.utils import HUB_WEB_ROOT
 from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task, nn, yaml_model_load
-from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
+from ultralytics.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks, emojis,
                                is_git_dir, yaml_load)
 from ultralytics.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
 from ultralytics.utils.downloads import GITHUB_ASSET_STEMS
@@ -448,11 +448,11 @@ def smart_load(self, key):
         """Load model/trainer/validator/predictor."""
         try:
             return self.task_map[self.task][key]
-        except Exception:
+        except Exception as e:
             name = self.__class__.__name__
             mode = inspect.stack()[1][3]  # get the function name.
             raise NotImplementedError(
-                f'WARNING ⚠️ `{name}` model does not support `{mode}` mode for `{self.task}` task yet.')
+                emojis(f'WARNING ⚠️ `{name}` model does not support `{mode}` mode for `{self.task}` task yet.')) from e
 
     @property
     def task_map(self):

ultralytics/engine/validator.py

@@ -51,9 +51,18 @@ class BaseValidator:
         device (torch.device): Device to use for validation.
         batch_i (int): Current batch index.
         training (bool): Whether the model is in training mode.
-        speed (float): Batch processing speed in seconds.
-        jdict (dict): Dictionary to store validation results.
+        names (dict): Class names.
+        seen: Records the number of images seen so far during validation.
+        stats: Placeholder for statistics during validation.
+        confusion_matrix: Placeholder for a confusion matrix.
+        nc: Number of classes.
+        iouv: (torch.Tensor): IoU thresholds from 0.50 to 0.95 in spaces of 0.05.
+        jdict (dict): Dictionary to store JSON validation results.
+        speed (dict): Dictionary with keys 'preprocess', 'inference', 'loss', 'postprocess' and their respective
+                      batch processing times in milliseconds.
         save_dir (Path): Directory to save results.
+        plots (dict): Dictionary to store plots for visualization.
+        callbacks (dict): Dictionary to store various callback functions.
     """
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
@@ -65,6 +74,7 @@ def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callba
             save_dir (Path): Directory to save results.
             pbar (tqdm.tqdm): Progress bar for displaying progress.
             args (SimpleNamespace): Configuration for the validator.
+            _callbacks (dict): Dictionary to store various callback functions.
         """
         self.dataloader = dataloader
         self.pbar = pbar
@@ -74,8 +84,14 @@ def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callba
         self.device = None
         self.batch_i = None
         self.training = True
-        self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
+        self.names = None
+        self.seen = None
+        self.stats = None
+        self.confusion_matrix = None
+        self.nc = None
+        self.iouv = None
         self.jdict = None
+        self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0}
 
         project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
         name = self.args.name or f'{self.args.mode}'
@@ -200,26 +216,26 @@ def __call__(self, trainer=None, model=None):
                 LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
             return stats
 
-    def match_predictions(self, pred_classes: torch.Tensor, true_classes: torch.Tensor,
-                          iou: torch.Tensor) -> torch.Tensor:
+    def match_predictions(self, pred_classes, true_classes, iou):
         """
         Matches predictions to ground truth objects (pred_classes, true_classes) using IoU.
 
         Args:
             pred_classes (torch.Tensor): Predicted class indices of shape(N,).
             true_classes (torch.Tensor): Target class indices of shape(M,).
+            iou (torch.Tensor): IoU thresholds from 0.50 to 0.95 in space of 0.05.
 
         Returns:
             (torch.Tensor): Correct tensor of shape(N,10) for 10 IoU thresholds.
         """
         correct = np.zeros((pred_classes.shape[0], self.iouv.shape[0])).astype(bool)
         correct_class = true_classes[:, None] == pred_classes
-        for i in range(len(self.iouv)):
-            x = torch.where((iou >= self.iouv[i]) & correct_class)  # IoU > threshold and classes match
-            if x[0].shape[0]:
+        for i, iouv in enumerate(self.iouv):
+            x = torch.nonzero(iou.ge(iouv) & correct_class)  # IoU > threshold and classes match
+            if x.shape[0]:
                 # Concatenate [label, detect, iou]
-                matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
-                if x[0].shape[0] > 1:
+                matches = torch.cat((x, iou[x[:, 0], x[:, 1]].unsqueeze(1)), 1).cpu().numpy()
+                if x.shape[0] > 1:
                     matches = matches[matches[:, 2].argsort()[::-1]]
                     matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
                     # matches = matches[matches[:, 2].argsort()[::-1]]

ultralytics/models/fastsam/val.py

@@ -44,7 +44,7 @@ def get_desc(self):
                                          'R', 'mAP50', 'mAP50-95)')
 
     def postprocess(self, preds):
-        """Postprocesses YOLO predictions and returns output detections with proto."""
+        """Post-processes YOLO predictions and returns output detections with proto."""
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf,
                                     self.args.iou,

ultralytics/models/nas/predict.py

@@ -11,7 +11,7 @@
 class NASPredictor(BasePredictor):
 
     def postprocess(self, preds_in, img, orig_imgs):
-        """Postprocesses predictions and returns a list of Results objects."""
+        """Postprocess predictions and returns a list of Results objects."""
 
         # Cat boxes and class scores
         boxes = xyxy2xywh(preds_in[0][0])

ultralytics/models/sam/predict.py

@@ -310,7 +310,7 @@ def setup_model(self, model, verbose=True):
         self.done_warmup = True
 
     def postprocess(self, preds, img, orig_imgs):
-        """Postprocesses inference output predictions to create detection masks for objects."""
+        """Post-processes inference output predictions to create detection masks for objects."""
         # (N, 1, H, W), (N, 1)
         pred_masks, pred_scores = preds[:2]
         pred_bboxes = preds[2] if self.segment_all else None

ultralytics/models/yolo/classify/predict.py

@@ -21,7 +21,7 @@ def preprocess(self, img):
         return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
 
     def postprocess(self, preds, img, orig_imgs):
-        """Postprocesses predictions to return Results objects."""
+        """Post-processes predictions to return Results objects."""
         results = []
         for i, pred in enumerate(preds):
             orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs

ultralytics/models/yolo/classify/train.py

@@ -43,11 +43,7 @@ def get_model(self, cfg=None, weights=None, verbose=True):
         return model
 
     def setup_model(self):
-        """
-        load/create/download model for any task
-        """
-        # Classification models require special handling
-
+        """load/create/download model for any task"""
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return
 
@@ -65,7 +61,7 @@ def setup_model(self):
             FileNotFoundError(f'ERROR: model={model} not found locally or online. Please check model name.')
         ClassificationModel.reshape_outputs(self.model, self.data['nc'])
 
-        return  # dont return ckpt. Classification doesn't support resume
+        return  # do not return ckpt. Classification doesn't support resume
 
     def build_dataset(self, img_path, mode='train', batch=None):
         return ClassificationDataset(root=img_path, args=self.args, augment=mode == 'train')
@@ -102,9 +98,9 @@ def get_validator(self):
 
     def label_loss_items(self, loss_items=None, prefix='train'):
         """
-        Returns a loss dict with labelled training loss items tensor
+        Returns a loss dict with labelled training loss items tensor. Not needed for classification but necessary for
+        segmentation & detection
         """
-        # Not needed for classification but necessary for segmentation & detection
         keys = [f'{prefix}/{x}' for x in self.loss_names]
         if loss_items is None:
             return keys
@@ -144,7 +140,7 @@ def plot_training_samples(self, batch, ni):
 
 
 def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train the YOLO classification model."""
+    """Train a YOLO classification model."""
     model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"
     data = cfg.data or 'mnist160'  # or yolo.ClassificationDataset("mnist")
     device = cfg.device if cfg.device is not None else ''

ultralytics/models/yolo/classify/val.py

@@ -14,6 +14,8 @@ class ClassificationValidator(BaseValidator):
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar."""
         super().__init__(dataloader, save_dir, pbar, args, _callbacks)
+        self.targets = None
+        self.pred = None
         self.args.task = 'classify'
         self.metrics = ClassifyMetrics()
 

ultralytics/models/yolo/detect/predict.py

@@ -10,7 +10,7 @@
 class DetectionPredictor(BasePredictor):
 
     def postprocess(self, preds, img, orig_imgs):
-        """Postprocesses predictions and returns a list of Results objects."""
+        """Post-processes predictions and returns a list of Results objects."""
         preds = ops.non_max_suppression(preds,
                                         self.args.conf,
                                         self.args.iou,

ultralytics/models/yolo/detect/train.py

@@ -13,7 +13,6 @@
 from ultralytics.utils.torch_utils import de_parallel, torch_distributed_zero_first
 
 
-# BaseTrainer python usage
 class DetectionTrainer(BaseTrainer):
 
     def build_dataset(self, img_path, mode='train', batch=None):
@@ -69,9 +68,9 @@ def get_validator(self):
 
     def label_loss_items(self, loss_items=None, prefix='train'):
         """
-        Returns a loss dict with labelled training loss items tensor
+        Returns a loss dict with labelled training loss items tensor. Not needed for classification but necessary for
+        segmentation & detection
         """
-        # Not needed for classification but necessary for segmentation & detection
         keys = [f'{prefix}/{x}' for x in self.loss_names]
         if loss_items is not None:
             loss_items = [round(float(x), 5) for x in loss_items]  # convert tensors to 5 decimal place floats

ultralytics/models/yolo/detect/val.py

@@ -20,9 +20,10 @@ class DetectionValidator(BaseValidator):
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
         """Initialize detection model with necessary variables and settings."""
         super().__init__(dataloader, save_dir, pbar, args, _callbacks)
-        self.args.task = 'detect'
+        self.nt_per_class = None
         self.is_coco = False
         self.class_map = None
+        self.args.task = 'detect'
         self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
         self.iouv = torch.linspace(0.5, 0.95, 10)  # iou vector for [email protected]:0.95
         self.niou = self.iouv.numel()
@@ -155,18 +156,23 @@ def print_results(self):
 
     def _process_batch(self, detections, labels):
         """
-        Return correct prediction matrix
-        Arguments:
-            detections (array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (array[M, 5]), class, x1, y1, x2, y2
+        Return correct prediction matrix.
+
+        Args:
+            detections (torch.Tensor): Tensor of shape [N, 6] representing detections.
+                Each detection is of the format: x1, y1, x2, y2, conf, class.
+            labels (torch.Tensor): Tensor of shape [M, 5] representing labels.
+                Each label is of the format: class, x1, y1, x2, y2.
+
         Returns:
-            correct (array[N, 10]), for 10 IoU levels
+            (torch.Tensor): Correct prediction matrix of shape [N, 10] for 10 IoU levels.
         """
         iou = box_iou(labels[:, 1:], detections[:, :4])
         return self.match_predictions(detections[:, 5], labels[:, 0], iou)
 
     def build_dataset(self, img_path, mode='val', batch=None):
-        """Build YOLO Dataset
+        """
+        Build YOLO Dataset.
 
         Args:
             img_path (str): Path to the folder containing images.

ultralytics/models/yolo/pose/train.py

@@ -8,7 +8,6 @@
 from ultralytics.utils.plotting import plot_images, plot_results
 
 
-# BaseTrainer python usage
 class PoseTrainer(yolo.detect.DetectionTrainer):
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):